Essential Skills for Analyzing Social Data

It is estimated that the average internet user spends a quarter of their time on social media sites each year. With over 400 million tweets a day and 10 million Facebook apps (and growing) the social frontier is evolving at an alarming rate – and with it comes exponentially more data from your target audience. According to Gleanster Research (see the Deep Dive “Leveraging Social Data Expertise to Maximize the Value of Social Listening“), 85% of brand marketers believe social media is critical to the marketing strategy. It turns out that it’s not so difficult to engage consumers on social; anybody can create a brand presence on social channels.

The difficulty is actually building relationships with customers and delivering engaging brand experiences, and that demands understanding, comprehension, judgment, interpretation, and, most importantly, listening. As the Greek philosopher Epictetus said, “We have two ears and one mouth so that we can listen twice as much as we speak.” Nowhere is this more important than in the realm of social media, where consumer options, desires, wants, and needs are readily available. But it’s one thing to listen, and it’s quite another to interpret and react appropriately – analysis, that is to say, often demands an entirely different set of skills. Brands have become increasingly adept at social listening, which means the data exists, but what do you do with it once you have it? What skills are required to extrapolate meaningful strategy and business decisions from social data?

Today, technology can definitely take the edge off the burden of analyzing social data. Social listening technologies extrapolate insights from real-time monitoring and machine learning. They deliver dashboards, audience analysis, interactive charts, and custom reporting. Some tools even come equipped with computational linguistics and natural text processing to tap into unstructured social data. But the technology, it turns out, is only part of the solution. According to Top Performing organizations who reported the highest revenue growth and marked increases in the quality and quantity of actionable insights derived from social data, the technology must also be supplemented with skilled experts to really be valuable. In fact, 91% of Top Performing marketers indicated participation by social data experts was a top two value driver for maximizing the return on investment in social listening platforms. Sometimes this expertise can come from internal resources, but over two-thirds of Top Performers relied on third-party experts in agencies and consultancies to help uncover the needles in the haystack inside mountains of unstructured social data.

Making Big Data Small

All this talk about big data definitely causes some eyes to roll. But big data isn’t just a buzzword, it’s a term that defines a very real issue that every business is facing with social data. The volume of customer data the average organization collects is growing at exponential rates. It’s growing so rapidly it is quickly outpacing the ability for one database to centralize and manage the data – making it more and more difficult to process insights from social data. Social data creates an infinite source of structured and unstructured data that can be very difficult to analyze. Sure, the insights are there; buyers are talking about their preferences, experiences, behavior, and affinities on social channels. But that data must be extracted from the noise, much like a diamond is extracted from coal. And like diamonds, what comes out of the ground is initially rough and ugly. It takes a trained diamond cutter to transform the stone into a valuable gem. It also takes an expert eye to interpret the bits of data that emerge in social listening platforms and transform them into marketing strategy and insights. Again, the goal is fostering long-term relationships with customers.

Figure 1: A Simple Framework for Mining the Insights in Social Data

Technology Check, Now What?

The nice thing about the new generation of social listening technologies is that they have virtually removed the guesswork and boring statistical analysis that would otherwise have brand marketers running for the hills. Today, the tools are designed to be used by business users, for good reason. The insights derived from social listening technologies are only valuable when they are put in context. That means you don’t need IT people or highly skilled statisticians to extrapolate really powerful business insights from social media data. Here are some ideas about what to look for when hiring resources to take on social listening and analysis:

Someone who has experience in the industry or is/was a previous buyer. The person interpreting social media insights for your firm should have a deep knowledge of the industry and the product value proposition. They don’t have to be statisticians or highly technical. Experience and knowledge of the one or more target audiences will be invaluable to the conclusions they draw from the data.

Someone who is inquisitive and likes solving puzzles. Social data is full of glimmers. Glimmers are little pieces of data that can be linked together to develop a bigger picture trend or insight. The answers aren’t always obvious, especially with respect to unstructured data. You want people who like sudoku, people who are hungry for challenges and always want to dig for more meaning in problems.

Someone who wants closure and cares about customer satisfaction. Social media analysis is not just about ascertaining high level trends, it’s also about monitoring issues. Most social listening platforms use sentiment analysis to quickly alert users of unhappy customers or customer issues, so the person in charge should have a good relationship with sales, service, and maybe even IT to close out customer issues with satisfactory resolutions.

34% of Top Performers conduct feature- and aspect-level analysis: Hone in on sentiment as it relates to specific attributes of the brand, product, and service.

27% of Top Performers conduct consumer segment level analysis: Classify social data according to demographic, psychographic, and other segmentation schemes to refine the segment based on audience behavior.

Ian Michiels is a Principal & CEO at Gleanster Research, a globally known IT Market Research firm covering marketing, sales, voice of the customer, and BI. Michiels is a seasoned analyst, consultant, and speaker responsible for over 350 published analyst reports. He maintains ongoing relationships with hundreds of software executives each year and surveys tens of thousands of industry professionals to keep a finger on the pulse of the market. Michiels has also worked with some of the world's biggest brands including Nike, Sears Holdings, Wells Fargo, Franklin Templeton, and Ceasars.

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